Datasets:
Libraries:
License:
| license: cc-by-4.0 | |
| tags: | |
| - croissant | |
| - weather-forecasting | |
| - extreme-weather | |
| - deep-learning | |
| - high-resolution | |
| # HR-Extreme Dataset | |
| ## Overview | |
| HR-Extreme is a high-resolution dataset designed to evaluate the performance of state-of-the-art models in predicting extreme weather events. The dataset contains 17 types of extreme weather events from 2020, based on High-Resolution Rapid Refresh (HRRR) data. It is intended for researchers in weather forecasting, encompassing both physical and deep learning methods. | |
| [Github Link](github_link: https://github.com/HuskyNian/HR-Extreme) | |
| ## Dataset Structure | |
| The dataset is divided into two main folders: | |
| - `202001_202006`: Contains data from January 2020 to June 2020. | |
| - `202007_202012`: Contains data from July 2020 to December 2020. | |
| Each folder stores the dataset in the WebDataset format, following Hugging Face's recommendations. Every 10 `.npz` files are aggregated into a single `.tar` file, named sequentially as `i.tar` (e.g., `0001.tar`). | |
| ## Usage | |
| To construct the dataset, use the provided scripts in the GitHub repository. The main script, `make_datasetall.py`, generates an index file for the dataset: | |
| ```bash | |
| python make_datasetall.py 20200101 20200630 | |